Irrigation Strategies Research Articles

Deficit drip irrigation improves kiwifruit quality and water productivity under rain-shelter cultivation in the humid area of South China

Comprehending crop responses to water deficit at different growth stages is crucial for developing effective irrigation strategies. Different water deficit treatments (WDTs) were applied to the kiwifruit vines to investigate the effect of water deficit during different growth stages on the fruit quality, yield, and water productivity (WP); subsequently, the technique for order preference by similarity to an ideal solution method (TOPSIS) was employed to determine optimal treatments for kiwifruit cultivation. A total of 17 irrigation treatments were applied, including one control treatment (CTL, full irrigation) and four WDTs (denoted as D15%, D25%, D35%, and D45% respectively) during the bud burst to leafing (I), flowering to fruit set (II), fruit expansion (III), and fruit maturation (IV) stages. Results showed that WDTs during I, II, III, and IV decreased evapotranspiration (ET) over the whole growth period of kiwifruit vines by 1.2–3.8, 1.5–4.4, 4.7–14.3, and 6.9–21.3% compared with CTL, respectively. WDTs during stages I and II increased fruit volume (Vf) and fruit weight (FW), while exhibiting no significant impact on yield, WP, and chemical quality of kiwifruit. WDTs during stage III improved fruit firmness (Fn), total soluble solids (TSS), and titratable acidity (TA); however, it also caused severe reduction in Vf, FW, yield, and WP. Appropriate WDTs during stage IV significantly improved Fn, TSS, TA, vitamin C (Vc), and WP without compromising Vf, FW, and yield of kiwifruit. The IV-D25% treatment was determined to be the optimal treatment for improving fruit quality and WP of kiwifruit while maintaining yield, which increased TSS, TA, Vc, and WP by 9.1, 6.1, 19.2, 4.6%, respectively; the combination of D25%, D25%, full irrigation, and D25% treatments during stages I, II, III, and IV should be a viable irrigation strategy to simultaneously achieve high yield, quality, and WP of kiwifruit.

Effect of using reclaimed water via furrow and subsurface drip systems under alternate partial root-zone irrigation mechanism on crops growth and soil properties

The growing population in the face of water scarcity inevitably necessitates the quest for alternative sources of irrigation water, which integrates them with irrigation strategies for improved agricultural productivity to meet the Sustainable Development Goals. A three-year field experiment was conducted in 2017, 2018, and 2019 to investigate the effect of water quality (reclaimed water (RW) and clean water (CW)), irrigation techniques (subsurface drip irrigation (SDI) and furrow irrigation (FUI)), irrigation methods (full irrigation (FI) and alternate partial root-zone irrigation (APRI) (70% ETc)), and their interactions on the fresh fruit yield (FY), irrigation water use efficiency (IWUE), and nitrogen use efficiency (NUE) of tomatoes. Further, electrical conductivity (EC), pH, and organic matter (OM) of soil were evaluated. The experiments were undertaken over three growing spring seasons in a greenhouse at the Chinese Academy of Agricultural Sciences in Henan Province, China. Throughout the three years of this study, the yield, the IWUE, and the NUE values of all treatments under RW were higher than those corresponding values under CW. The trend was the same under SDI as it was under FUI. Statistical analyses revealed that there was no significant effect (P > 0.05) of water quality, irrigation technique, and irrigation methods on the soil EC, pH, and OM over the three years. In addition, the interaction between the different experimental factors over the three years of the study was not significant. In conclusion, the application of RW under SDI can result in saving CW and increasing productivity without any negative effect on the investigated soil properties. Furthermore, when RW-SDI is used in conjunction with APRI, it can result in increasing IWUE.

Estimating non-productive water loss in irrigated farmland in arid oasis regions: Based on stable isotope data

In arid oasis regions, water resources are severely scarce, with agricultural water usage far exceeding that of more humid areas. As a result, assessing non-productive water losses in farmland becomes crucial for estimating the water requirements reliant on irrigation for oasis cultivation. From April 2018 to October 2021, we established an observation system in the Minqin Oasis farmland. By utilizing hydrogen and oxygen isotopes in precipitation, soil water, and maize stem xylem water, we quantified the non-productive water loss (FE) and soil water infiltration rate (fe) in maize fields. Our findings indicate: (1) The average non-productive losses from rainfall and irrigation in arid maize fields were 39 %, with peak losses reaching 58 %; (2) Non-productive losses due to evapotranspiration were mainly observed in June and July, while losses from infiltration predominantly occurred in April-May and August-September; (3) Crop evapotranspiration capacity emerged as a significant factor influencing evaporation losses, while individual irrigation amounts and rainfall determined soil infiltration losses. We believe that judiciously managing irrigation volume and adjusting irrigation strategies in arid farmlands could unlock substantial potential for conserving water resources.

Integrating GIS and remote sensing for land use/land cover mapping and groundwater potential assessment for climate-smart cocoa irrigation in Ghana

Although Ghana is a leading global cocoa producer, its production and yield have experienced declines in recent years due to various factors, including long-term climate change such as increasing temperatures and changing rainfall patterns, as well as drought events. With the increasing exposure of cocoa-producing regions to extreme weather events, the vulnerability of cocoa production is also expected to rise. Supplemental irrigation for cocoa farmers has emerged as a viable adaptation strategy to ensure a consistent water supply and enhance yield. However, understanding the potential for surface and groundwater irrigation in the cocoa-growing belt remains limited. Consequently, this study aims to provide decision-support maps for surface and groundwater irrigation potential to aid planning and investment in climate-smart cocoa irrigation. Utilizing state-of-the-art geospatial and remote sensing tools, data, and methods, alongside in-situ groundwater data, we assess the irrigation potential within Ghana's cocoa-growing areas. Our analysis identified a total area of 22,126 km2 for cocoa plantations and 125.2 km2 for surface water bodies within the cocoa-growing regions. The multi-criteria analysis (MCA) revealed that approximately 80% of the study area exhibits moderate to very high groundwater availability potential. Comparing the MCA output with existing borehole locations demonstrated a reasonable correlation, with about 80% of existing boreholes located in areas with moderate to very high potential. Boreholes in very high potential areas had the highest mean yield of 90.7 l/min, while those in low groundwater availability potential areas registered the lowest mean yield of 58.2 l/min. Our study offers a comprehensive evaluation of water storage components and their implications for cocoa irrigation in Ghana. While groundwater availability shows a generally positive trend, soil moisture and surface water have been declining, particularly in the last decade. These findings underline the need for climate-smart cocoa irrigation strategies that make use of abundant groundwater resources during deficit periods. A balanced conjunctive use of surface and groundwater resources could thus serve as a sustainable solution for maintaining cocoa production in the face of climate change.

Assessment of Crop-Water Requirement of Alfalfa Using FAO-CROPWAT Model-8.0

The current study was conducted at the Farming System Research Station, situated within the Department of Agronomy at the College of Agriculture, Junagadh Agricultural University in Junagadh, Gujarat, India. This research took place during the winter cycle (Rabi season) 2021 to 2022 and 2022 to 2023. In this study, the CROPWAT crop irrigation schedule provides an overall Evapotranspiration (ETc) value of 549.6 millimeters as the optimal water requirement for achieving maximum crop production with sufficient water supply. The actual water use by the crop during its growth period amounted to 544.2 millimeters and total net irrigation applied was 507.9 millimeters, while the gross irrigation amounted to 725.6 millimeters. The irrigation efficiency was 100%. Comparing this approach to irrigation scheduling based on the Irrigation Water to Crop Potential Evapotranspiration (IW/CPE) ratio, using CROPWAT enabled the precise management of irrigation water. It ensured that the alfalfa crop received the appropriate amount of water according to its specific growth stage requirements. The CROPWAT model data indicates that there was no yield reduction, even with a 100% critical depletion of available soil moisture. Additionally, the seasonal yield response factor was calculated to be 0.80%. These findings emphasize the effectiveness of the irrigation strategy employed, resulting in optimal crop yields without yield losses due to critical soil moisture depletion.

Predicting Crop Evapotranspiration under Non-Standard Conditions Using Machine Learning Algorithms, a Case Study for Vitis vinifera L. cv Tempranillo

This study focuses on assessing the accuracy of supervised machine learning regression algorithms (MLAs) in predicting actual crop evapotranspiration (ETc act) for a deficit irrigated vineyard of Vitis vinifera cv. Tempranillo, influenced by a typical Mediterranean climate. The standard approach of using the Food and Agriculture Organization (FAO) crop evapotranspiration under standard conditions (FAO-56 Kc-ET0) to estimate ETc act for irrigation purposes faces limitations in row-based, sparse, and drip irrigated crops with large, exposed soil areas, due to data requirements and potential shortcomings. One significant challenge is the accurate estimation of the basal crop coefficient (Kcb), which can be influenced by incorrect estimations of the effective transpiring leaf area and surface resistance. The research results demonstrate that the tested MLAs can accurately estimate ETc act for the vineyard with minimal errors. The Root-Mean-Square Error (RMSE) values were found to be in the range of 0.019 to 0.030 mm·h⁻¹. Additionally, the obtained MLAs reduced data requirements, which suggests their feasibility to be used to optimize sustainable irrigation management in vineyards and other row crops. The positive outcomes of the study highlight the potential advantages of employing MLAs for precise and efficient estimation of crop evapotranspiration, leading to improved water management practices in vineyards. This could promote the adoption of more sustainable and resource-efficient irrigation strategies, particularly in regions with Mediterranean climates.

Enhancing the Fruit Yield and Quality in Pomegranate: Insights into Drip Irrigation and Mulching Strategies.

Pomegranate (Punica granatum L.) is a fruit tree that is globally distributed, especially in warm areas with low annual rainfall and limited water availability. This species exemplifies the critical role of water in agriculture and the need for efficient irrigation practices due to its characteristics, cultivation requirements, and geographic diffusion. In this study, we investigated the effects of drip irrigation and mulching on the vegetative growth, yield, and fruit quality attributes of pomegranate. The experiment involved three irrigation regimes (100% of evapotranspiration, 80%, and 60%) and three mulching treatments (no mulch, plastic mulch, and organic mulch) in a factorial combination. Both irrigation and mulching had significant positive influences on the yield and fruit quality attributes. Specifically, deficit irrigation strategies showed a negative impact on the fruit yield per tree, with a greater effect observed as the severity of the irrigation deficit increased. Mulching, on the other hand, led to a significant increase in the fruit yield, primarily attributed to an increase in fruit size. Furthermore, the analysis indicated that irrigation and mulching treatments had distinct effects on fruit traits such as the fruit length, width, volume, and rind thickness. Interestingly, the study highlighted that the effects of irrigation and mulching on fruit quality attributes were mostly independent of each other, suggesting an additive influence rather than an interaction between the two factors. These findings underscore the importance of considering irrigation and mulching practices for optimizing fruit quality in pomegranate cultivation, particularly in semi-arid regions. The results contribute valuable insights for farmers and researchers seeking to enhance fruit production and quality.

Biochar amendment combined with partial root-zone drying irrigation alleviates salinity stress and improves root morphology and water use efficiency in cotton plant

An adsorption experiment and a pot experiment were executed in order to explore the mechanisms by which biochar amendment in combination with reduced irrigation affects sodium and potassium uptake, root morphology, water use efficiency, and salinity tolerance of cotton plants. In the adsorption experiment, ten NaCl concentration gradients (0, 50, 100, 150, 200, 250, 300, 350, 400, and 500 mM) were set for testing isotherm adsorption of Na+ by biochar. It was found that the isotherms of Na+ adsorption by wheat straw biochar (WSP) and softwood biochar (SWP) were in accordance with the Langmuir isotherm model, and the Na+ adsorption ability of WSP (55.20 mg g−1) was superior to that of SWP (47.38 mg g−1). The pot experiment consisted three factors, viz., three biochar amendments (no biochar, WSP, and SWP), three irrigation strategies (deficit irrigation, partial root-zone drying irrigation – PRD, full irrigation), and two NaCl concentrations gradients (0 mM and 200 mM). The findings indicated that salinity stress lowered K+ concentration, root length, root surface area, and root volume (RV), but increased Na+ concentration, root average diameter, and root tissue density. However, biochar amendment decreased Na+ concentration, increased K+ concentration, and improved root morphology. In particular, the combination of WSP and PRD increased K+/Na+ ratio, RV, root weight density, root surface area density, water use efficiency, and partial factor productivity under salt stress, which can be a promising strategy to cope with drought and salinity stress in cotton production.

Threshold Values of Plant Water Status for Scheduling Deficit Irrigation in Early Apricot Trees

Irrigated agriculture is facing a serious problem of water scarcity, which could be mitigated by optimizing the application of regulated deficit irrigation (RDI) strategies. For this reason, the aim of our study was to determine irrigation thresholds based on direct water status indicators of apricot trees under RDI to maximize water productivity. Three treatments were tested: (i) Control (CTL), irrigated at 100% of the crop evapotranspiration (ETc) during the entire crop cycle; (ii) RDI1, irrigated as CTL, except during fruit growth stages I–II when irrigation was reduced by 20% of CTL, and during late post-harvest, with an irrigation threshold of a moderate water stress of −1.5 MPa of stem water potential (Ψs); and (iii) RDI2, irrigated as RDI1, but during late post-harvest using a severe water stress threshold of −2.0 MPa of Ψs. As the irrigation scheduling of RDI1 and RDI2 did not affect yield and fruit quality, the crop water productivity was increased by 13.2 and 25.6%, respectively. This corresponded to 1124 and 2133 m3 ha−1 of water saved for RDI1 and RDI2. A water stress integral of 30.2 MPa day during post-harvest could be considered optimal since when 41 MPa day was accumulated, vegetative growth was reduced by 35%. The non-sensitive periods to water deficit were delimited by the accumulation of growing degree days (GDD) from full bloom, the end of fruit growth stages I–II corresponded to an accumulation of 640 °C GDD, and the beginning of the late post-harvest to an accumulation of 1840 °C GDD.

Acoustic signal-based indigenous real-time rainfall monitoring system for sustainable environment

The rainfall weather station employs a tipping bucket rain gauge, which serves as a specialized instrument for the meticulous assessment and documentation of various rainwater parameters. The implementation of a tipping bucket rain gauge for rainfall monitoring bears significant implications for both societal productivity as well as improvement of human life. A noteworthy example can be the constructive influence of rainwater over the sustainable agricultural irrigation practices, wherein the precise monitoring of rainfall through a tipping bucket rain gauge enables the formulation of tedious irrigation strategies. The rainfall monitoring if often handle using rain gauge which majorly faces two challenges named as mechanical devices failure and high installation and maintenance cost. Considering the challenges, we propose the fully automated rain gauge (RG) based on the principle of sound and its properties for rainfall monitoring. The working prototype is part of our work whose primary task is to collect the rainfall acoustic value and store it in the cloud. Our mechanism is to use the acoustic property of rain data to categorize rainfall intensity. We perform blind signal separation on the received signal (acoustic signal recorded with the help of microphone sensor) and feed the separated signal to a recurrent convolution neural network (RCNN). The source separation of the collected acoustic signals is primarily being done using independent component analysis and principal components analysis. The proposed solution can be able to make the classification of rain intensity with more than 80% accuracy. In addition to this, the developed method provides the sustainable solution to the challenges with the low-cost and application-specific acceptable threshold criteria and supplement rain measurement techniques.

Napier yield response under different irrigation strategies in a tropical setting

Napier yield response under different irrigation strategies in a tropical setting

Deficit irrigation strategies of flat peach trees under semi-arid conditions

Deficit irrigation strategies of flat peach trees under semi-arid conditions

Onion (Allium cepa L.) Production Guide Simulation Program using C Programming

In addition to environmental factors, the success of onion cultivation also depends on adopting sophisticated agricultural techniques and methods. Crop simulation models are now required to enhance onion cultivation, reduce risks, and increase yields. This study aimed to develop and evaluate a rudimentary C programming language-based onion production simulation software. This research created a simulation of a guide for producing onions. This simulation tool integrates agricultural expertise with technology to provide onion producers, researchers, and agricultural stakeholders with valuable insights and guidance. The simulation assists users in optimizing onion cultivation from planting to harvest through an in-depth look at best practices. The simulation incorporates essential facets of onion cultivation, such as planting techniques, irrigation strategies, insect and disease management, and market-driven decision-making. Advantages of the simulation include precise financial planning, resource allocation, and risk management. Users gain a greater comprehension of expected income, allowing them to budget more effectively and improve their market access strategies. In addition, the simulation encourages adopting sustainable agricultural practices, technological integration, and innovation in response to onion producers' evolving challenges.

Soil Fertility Prediction

Abstract: Soil fertility plays a important role in figuring out agricultural productiveness and sustainability. Traditional methods of assessing soil fertility involve time-consuming and high-priced laboratory tests, restricting their scalability and real-time applicability. To overcome these challenges, this study proposes a data-driven method utilizing machine learning techniques for accurate and efficient soil fertility prediction. Several machine learning algorithms, inclusive of decision trees, Random Forests, k nearest neighbors, and Gradient Boosting Machines (GBM), are employed to model the complex relationships among soil properties and fertility. Feature selection techniques are carried out to identify the most influential soil parameters for enhanced prediction accuracy and reduced model complexity.The outcomes demonstrate that machine learning models can appropriately predict soil fertility, outperforming traditional approaches in terms of speed and cost-effectiveness. Moreover, the characteristic selection process identifies key soil properties that have the most significant effect on fertility, offering valuable insights for agricultural decision-making and targeted soil management. The proposed approach offers potential applications in precision agriculture, enabling farmers to make knowledgeable choices regarding crop selection, nutrient management, and irrigation strategies based totally on actual-time soil fertility predictions. By optimizing resource allocation and minimizing environmental influences, this data-driven solution contributes to the promotion sustainable agricultural practices and guarantees food safety for a growing global population. These are the essential nutrients that the crop requires for its growth pH nitrogen(N), phosphorus(P), potassium(K), CaCo3, Organic Carbon, Organic matter, CEC (Cation exchange capacity) .

Uptake and accumulation of emerging contaminants in processing tomato irrigated with tertiary treated wastewater effluent: a pilot-scale study.

The reuse of treated wastewater for crop irrigation is vital in water-scarce semi-arid regions. However, concerns arise regarding emerging contaminants (ECs) that persist in treated wastewater and may accumulate in irrigated crops, potentially entering the food chain and the environment. This pilot-scale study conducted in southern Italy focused on tomato plants (Solanum lycopersicum L. cv Taylor F1) irrigated with treated wastewater to investigate EC uptake, accumulation, and translocation processes. The experiment spanned from June to September 2021 and involved three irrigation strategies: conventional water (FW), treated wastewater spiked with 10 target contaminants at the European average dose (TWWx1), and tertiary WWTP effluent spiked with the target contaminants at a triple dose (TWWx3). The results showed distinct behavior and distribution of ECs between the TWWx1 and TWWx3 strategies. In the TWWx3 strategy, clarithromycin, carbamazepine, metoprolol, fluconazole, and climbazole exhibited interactions with the soil-plant system, with varying degradation rates, soil accumulation rates, and plant accumulation rates. In contrast, naproxen, ketoprofen, diclofenac, sulfamethoxazole, and trimethoprim showed degradation. These findings imply that some ECs may be actively taken up by plants, potentially introducing them into the food chain and raising concerns for humans and the environment.

Değişen Sulama Suyu Seviyeleriyle Toprağa Farklı Oranlarda Biyoçar Uygulanarak Yetiştirilen Kıvırcık Marulun Fiziksel ve Fizyolojik Özellikleri ile Mineral Madde İçeriği

The crisis experienced from the water supply causes food production to be adversely affected in the agriculture sector, which is the biggest consumer of water. The deficit irrigation strategy ensures the continuity of food production as well as using water effectively. However, since the plant grown in this strategy is exposed to abiotic stress, it experiences significant yield and quality losses. For this reason, it is necessary to develop approaches to improve yield and quality losses of the plant grown with deficit irrigation. In this study, the physical and physiological properties and mineral content of curly lettuce (Lactuca sativa L. var. Crispa) grown by applying different rates of biochar to the soil (0%, 1%, 2%, 3%) with varying irrigation water levels (100%, 67%, 33%) researched. The study determined that decreasing irrigation water level decreased plant height, stem diameter, number of leaves, root wet and dry weights, plant weight, chlorophyll and leaf relative water contents, stomatal conductivity and N, P, K, Ca, Mg, Na, Fe, Cu, Mn and Zn contents of curly lettuce, while increasing the membrane damage index, but the physical and physiological properties and mineral content affecting the yield and quality of curly lettuce improved with increasing biochar rates. In the study, as a result of the emergence of the highest stress factor in irrigation at 33% level, it was observed that physical and physiological properties and mineral content of curly lettuce were affected at the highest level, and the dose of biochar, which managed the stress most effectively, was 3%. As a result, considering that biochar has an important potential to improve yield and quality losses of curly lettuce grown under deficit irrigation conditions, the use of biochar in the deficit irrigation regime were found to be recommendable.

A planning strategy for sprinkler-based variable rate irrigation

Automated variable rate irrigation (VRI) is one of the frontiers for sustainable water use in agriculture. In this paper, we describe a planning procedure for self-propelled rain gun sprinklers, a type of equipment largely used in irrigation of open field crops thanks to its relatively low cost and easy deployment in multiple sites. The sprinkler is commanded by an information system which acquires data from remote sensors (satellite), processes vegetation indices and spatial maps describing the water need of the crop, and computes a VRI prescription map for a specific field, i.e. the water quantity to be supplied to each field sub-area. The VRI prescription map is used to compute the motion commands for a rain gun system pulled by a hose reel. The procedure is based on a sprinkler model, has been validated by simulation, and is fully automated. Experiments have shown the precision in water distribution achievable with software-controlled sprinklers and effective planning strategies.

The effect of pixel heterogeneity on surface heat and water vapor flux estimated by the remote sensing-based model coupled with deep learning

Accurate quantification of sensible (H) and latent (LE) heat fluxes are desperately crucial for agricultural irrigation strategy, drought monitoring, water resource management, and climate change. Regional-scale H and LE were derived generally from satellite remote sensing data using relevant modeling methods. However, one of the challenges of modeling methods is to deal with sub-pixel heterogeneity, because the model usually assumes homogeneous conditions within a pixel. In this study, a hybrid model, which coupled deep neural network (DNN) and the remote sensing-based two-source energy balance (TSEB) model, was developed to estimate H and LE at multiple spatial resolutions to further quantify the influence of pixel size on the modeled fluxes. First, the DNN-based land surface temperature (LST) estimation model was developed and validated. LST at multiple spatial resolutions (i.e., 1, 5, 10, 30, 100, and 1000 m) were generated by the DNN method using multi-source satellite data including the GaoFen-2 (GF-2, a Chinese high-resolution optical remote-sensing satellite), Sentinel-2, and Landsat-8 images, respectively. Then, the hybrid DNN-TSEB model was used to estimate H and LE at multi-pixel sizes. Finally, modeled H and LE at 1 ∼ 1000 m were validated by eddy covariance (EC) and optical-microwave scintillometer (OMS) flux observation. Results revealed that the DNN-based LST model was a potential approach to generate LST at multiple pixel sizes. The hybrid DNN-TSEB model can produce more accurate H and LE when different land cover types were discriminated over heterogeneous farmland (e.g., the average bias for H and LE at a spatial resolution of 1 m were −9 and −6 W/m−2, respectively). Besides, the deviation of modeled fluxes from the observations increased as the spatial resolution became coarser, particularly the pixel size was greater than 30 m (average bias for H and LE were −34 and 30 W m−2, respectively). However, the magnitude of bias of fluxes estimates remained unchanged when the pixel sizes of satellite data were between 100 and 1000 m. The more important finding was that the hybrid model was a promising approach, which can obtain accurate estimates of H and LE at a high spatial resolution. In addition, the performance of the model based on GF-2 data was better than that based on Sentinel-2 and Landsat-8 data. The results suggest that pixel heterogeneity should be considered when estimating H and LE over heterogeneous landscapes with various surface types.

Performance Evaluation of Drip Irrigation System for Green Pepper (Capsicum annum, L.) Production in the Tamale Metropolis of Ghana

Performance evaluation is a diagnostic tool used to identify problem areas in an irrigation system and to indicate where improvements can be made in an existing irrigation system. Performance evaluation enables irrigation managers to redesign, improve upon the design and the irrigation strategies used. This study was carried out to assess the performance of a drip irrigation system installed and managed by Motor-King Company Limited in the Tamale Metropolis. The drip irrigation system consisted of two (2) reservoirs; 1500 litres and 1000 litres polytanks mounted at 1.2 m and 3.6 m high respectively. The drip irrigation system had a 32 mm main line and 16 mm lateral line with 2.7 l/h emitters spaced at 0.3 m. Three (3) laterals and nine (9) emitters were randomly selected in each plot. Emitter discharge was measured using catch cans. Five (5) performance indicators such as emitter flow variation, co-efficient of emitter flow variation, application efficiency, distribution uniformity and statistical uniformity were assessed. From the results, co-efficient of variation (7%) was rated good, emitter flow variation (18%) was rated acceptable, application efficiency was excellent (97.0%), low quarter distribution uniformity was good (87.2%), statistical uniformity was excellent (93.0%). These results imply that the drip irrigation performed satisfactorily and can still be used to produce vegetables with minimal losses.

Irrigation Strategies with Controlled Water Deficit in Two Production Cycles of Cotton.

Water scarcity is one of the main abiotic factors that limit agricultural production. In this sense, the identification of genotypes tolerant to water deficit associated with irrigation management strategies is extremely important. In this context, the objective of this study was to evaluate the morphology, production, water consumption, and water use efficiency of colored fiber cotton genotypes submitted to irrigation strategies with a water deficit in the phenological phases. Two experiments were conducted in succession. In the first experiment, a randomized block design was used in a 3 × 7 factorial scheme, corresponding to three colored cotton genotypes (BRS Rubi, BRS Jade, and BRS Safira) in seven irrigation management strategies with 40% of the real evapotranspiration (ETr) varying the phenological stages. In the second experiment, the same design was used in a 3 × 10 factorial arrangement (genotypes × irrigation management strategies). The water deficit in the vegetative phase can be used in the first year of cotton cultivation. Among the genotypes, 'BRS Jade' is the most tolerant to water deficit in terms of phytomass accumulation and fiber production.